Neural networks: a systematic introduction
Neural networks: a systematic introduction
Quantitative evaluation of color image segmentation results
Pattern Recognition Letters
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Digital Image Processing
Color Image Segmentation for Multimedia Applications
Journal of Intelligent and Robotic Systems
Multiresolution Color Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
SOM Ensemble-Based Image Segmentation
Neural Processing Letters
Tuning range image segmentation by genetic algorithm
EURASIP Journal on Applied Signal Processing
Segmentation of High-Resolution Remote Sensing Image Based on Marker-Based Watershed Algorithm
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04
Colour image segmentation using homogeneity method and data fusion techniques
EURASIP Journal on Advances in Signal Processing - Image processing and analysis in biomechanics
Colour image segmentation using the self-organizing map and adaptive resonance theory
Image and Vision Computing
Efficient grey-level image segmentation using an optimised MUSIG (OptiMUSIG) activation function
International Journal of Parallel, Emergent and Distributed Systems
Color Image Segmentation Based on Mean Shift and Normalized Cuts
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Segmentation of color images using multiscale clustering and graph theoretic region synthesis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Self-organization for object extraction using a multilayer neural network and fuzziness measures
IEEE Transactions on Fuzzy Systems
Image segmentation with ratio cut
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color clustering and learning for image segmentation based on neural networks
IEEE Transactions on Neural Networks
Multi-level image thresholding by synergetic differential evolution
Applied Soft Computing
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Segmentation of the different feature based data in a dataset is a challenging proposition in the image processing community. There exist different techniques to solve this problem satisfactorily. A color image is an example of three-dimensional dataset and it consists of a collection of three primary color intensity features. In this article, we focus on the segmentation of true color test images, based on all possible combination of color intensity features. A multilevel sigmoidal (MUSIG) activation function that is applied in the self-organizing neural network architecture is quite efficient enough to segment multilevel gray level intensity images. The function uses equal and fixed class responses, ignoring the heterogeneity of image information content. The optimized version of MUSIG (OptiMUSIG) activation function for the self-organizing neural network architecture can be generated with the optimized class responses from the image content and can be used to effectively segment multilevel gray level intensity images as well. This article proposes a parallel version of the OptiMUSIG (ParaOptiMUSIG) activation function with the optimized class responses for the individual features with a parallel self-organizing neural network architecture to segment true color images. The optimized class responses are generated in parallel using a genetic algorithm based optimization technique. A standard objective function is applied to measure the quality of the segmented images in the proposed genetic algorithm-based optimization method. Results of segmentation of two real life true color images by the ParaOptiMUSIG activation function show better performances over those obtained with a conventional non-optimized MUSIG activation applied separately on the color gamut.